Is Gemini 4 Hydrogen the everyday model for your coding agent?

Gemini 4 Hydrogen came out on 12 November as Google's fast model for coding agents, and the first reaction on r/singularity to the Artificial Analysis chart was blunt: "Am i misunderstanding the graph or is just Sol 6.1 but worse in every way". The top reply: "Speed is the main thing missing, it's significantly faster". The numbers back the first reading more than the second. Hydrogen's tokens are cheaper than GPT-6.1 Sol's and Claude Sonnet 5.5's, but it writes so many of them that a finished task costs more than Sol's. Whether Hydrogen should be your coding agent's everyday model depends on what it runs now. Coming from Sonnet 5.5, it's cheaper and faster, though Sol is cheaper still. Coming from Sol, you pay more per task for a faster first answer, fewer made-up answers and video input.

TL;DR: Gemini 4 Hydrogen at high scores 51 on the Artificial Analysis Intelligence Index for $1.87 a task, beating Claude Sonnet 5.5, which needs xhigh to reach 50 at $4.09. GPT-6.1 Sol at high scores 50 for $0.59 in about the same time, so Sol stays the cheaper everyday model. Pick Hydrogen for a faster first answer or video input.

How good is Gemini 4 Hydrogen?

Gemini 4 Hydrogen scores 51 on the Artificial Analysis Intelligence Index (opens in a new tab) at its default high thinking level, and 54 at the new xhigh level. The index runs nine evaluations on Artificial Analysis's own harness, including Terminal-Bench 4.0 (real terminal tasks) and FrontierCode 2.0 (code changes open-source maintainers would merge). For scale, Gemini 4 Argon, Google's flagship six weeks ago, scores 50, and Gemini 4 Carbon, the current one, scores 60.

Google charges $1.50 per million input tokens and $7.50 per million output, flat across the 1M context window, with a free tier in AI Studio (Google's docs (opens in a new tab)). GPT-6.1 Sol and Claude Sonnet 5.5 both list at $2 and $10, so Hydrogen's output tokens are 25% cheaper.

An agent doesn't buy tokens one at a time. It pays for a finished task, and a task costs whatever the model writes on the way to an answer. Google bills thinking as output tokens, at the output price.

How does Gemini 4 Hydrogen compare with GPT-6.1 Sol and Sonnet 5.5 per task?

Artificial Analysis runs every thinking level as its own model and reports what one task from its index cost and how long it took, averaged across the nine evaluations. Here are the three models from the deepest setting down:

Against Sonnet 5.5, Hydrogen is cheaper and faster at every score. Hydrogen at high beats Sonnet at xhigh by a point for less than half the cost and about half the time. At the top, Hydrogen at xhigh lands a point under Sonnet at max for a third of the price, which is how Artificial Analysis summed it up (opens in a new tab).

Against GPT-6.1 Sol, the same table turns. Sol at high scores 50, a point under Hydrogen at high, in 7.7 minutes to Hydrogen's 7.9, for $0.59. Hydrogen costs more than three times as much for that point. One index point is a small gap; three times the bill is not. Sol at xhigh scores higher than Hydrogen at high and still costs well under half. Hydrogen at medium is the fastest row in the table at 3.7 minutes, and Sol at medium still beats it on score and cost for 0.8 minutes more.

Why does a cheaper token cost more per task?

Gemini 4 Hydrogen writes far more than its rivals. Running the Intelligence Index at high, it produced 110 million output tokens (opens in a new tab), against 24.7 million for GPT-6.1 Sol (opens in a new tab) at high: four and a half times as much text, thinking included. At xhigh it produced 304 million (opens in a new tab), where the median for models in its price range is 72.8 million. "300M+ output tokens for that is ridiculous", as one reply under the chart put it.

A 25% discount per token can't cover four and a half times the tokens, and that is the gap between the launch price and the bill.

Hydrogen also writes fast, about 180 tokens a second on Artificial Analysis's runs, so the volume doesn't show up as waiting. At high it finishes an index task in about the same time as Sol at high. The extra text shows up on the invoice, and barely on the clock.

The setting you choose matters more than usual. Google's docs warn that xhigh "can cause overthinking on simpler prompts" and suggest testing all four levels, then picking the lowest that meets your quality bar. Going from high to xhigh on Hydrogen buys three index points for 70% more money per task. For everyday edits and test runs, leave it on high or try medium.

When is Gemini 4 Hydrogen the right everyday model?

Speed is Hydrogen's case, and it's narrower against Sol than the launch suggests. Against Claude it's clear: on Arena's Agent leaderboard (opens in a new tab), which ranks models on real agent sessions, Hydrogen at high debuted fifth and was the fastest model in the top 10, with a median 5.3 minutes per task, three times faster than first-placed Claude Fable. Against Sol, Artificial Analysis puts the two within 0.2 minutes per task at high. What differs is the wait for the first answer: 16.7 seconds for Hydrogen at high, 35.8 for Sol. If you watch your agent work turn by turn, those 19 seconds per turn are the speed you feel. At medium it flips, and Sol answers first in 4.6 seconds.

It also makes things up less. On Artificial Analysis's Omniscience hallucination test (opens in a new tab), Hydrogen at xhigh has an 8% hallucination rate, the third lowest measured; only Google's own Carbon and Argon, at 4%, score lower. That matters most when your agent answers questions about the codebase or summarises logs, where a wrong answer reads exactly like a right one.

Two more reasons are specific to Hydrogen. It takes speech and video as input, where Sol and Sonnet take text and images, so an agent can read a screen recording of a bug without a second model. And Google built it to pair with Carbon: in Antigravity, its Orchestrator lets Carbon plan and Hydrogen edit and run tests. Google says that reached near Carbon's success rate on Terminal-Bench 4.0 with 24% less quota and 19% faster. That's Google's number, Carbon's standalone score isn't published, and Orchestrator is limited to Google AI Ultra subscribers for now.

CursorBench, Cursor's own coding benchmark, puts Hydrogen at 54.3% at default effort (opens in a new tab), up from 39.6% for Gemini 3.8 Flash. If your agent still runs 3.8 Flash, move it.

What raises the bill when your agent calls Gemini 4 Hydrogen directly?

In Cursor or Antigravity, moving to Gemini 4 Hydrogen is a model picker change. If your own agent loop calls the Gemini API, two changes break it outright: declare tools through function calling, since a prompt asking for raw JSON tool calls can return MALFORMED_FUNCTION_CALL, and send earlier model turns back with their thought signatures unchanged. Three more items in Google's migration checklist (opens in a new tab) decide whether the per-task numbers above hold for you:

• The default thinking level is high. Gemini 3.8 Flash defaulted to medium, so the same request now thinks more and bills more. For short edits and test runs, set thinking_level to medium and measure before you accept the default.
• Explicit caching is gone. Hydrogen doesn't support cachedContents, so remove it. Implicit caching takes 95% off cached input on prompts of at least 4,096 tokens, so keep the system prompt and repo context at the front of every request, unchanged, and let the cache find it.
• Thinking now counts against the output cap. A max_output_tokens of 8,192 carried over from an older Flash setup will cut answers off mid-task. Google suggests 65,536, the synchronous limit.

What to do this week

Check which model your coding agent runs for everyday work. If it's Claude Sonnet 5.5, run your next ten routine tasks on Gemini 4 Hydrogen at high and on GPT-6.1 Sol at high, and compare the bill and the pass rate: on Artificial Analysis's numbers both beat Sonnet, and Sol does it for a third of Hydrogen's cost. If it's already Sol, stay, and keep Hydrogen for sessions where waiting is the cost that hurts, or where the agent has to watch a video. If your own loop calls the API, set the thinking level and keep repo context at the front of every request before you compare. I didn't run these benchmarks myself; every number comes from the sources linked above.

I write about coding agents and the models behind them on the gabs.app blog. I'm a full-stack engineer and my work is on gabs.app; if you're hiring, the contact form comes straight to me.